function result = probit(y,x,maxit,tol)
% PURPOSE: computes Probit Regression
%---------------------------------------------------
% USAGE: results = probit(y,x,maxit,tol)
% where: y = binary dependent variable vector (nobs x 1)
%        x = independent variables matrix (nobs x nvar)
%    maxit = optional (default=100)
%      tol = optional convergence (default=1e-6)
%---------------------------------------------------
% RETURNS: a structure
%        result.meth   = 'probit'
%        result.beta   = bhat
%        result.tstat  = t-stats
%        result.yhat   = yhat
%        result.resid  = residuals
%        result.sige   = e'*e/n
%        result.r2mf   = McFadden pseudo-R^2
%        result.rsqr   = Estrella R^2
%        result.lratio = LR-ratio test against intercept model
%        result.lik    = unrestricted Likelihood
%        result.cnvg   = convergence criterion, max(max(-inv(H)*g))
%        result.iter   = # of iterations
%        result.nobs   = nobs
%        result.nvar   = nvars
%        result.zip    = # of 0's
%        result.one    = # of 1's
%        result.y      = y data vector
% --------------------------------------------------
% SEE ALSO: prt_reg(results), logit(), tobit()
%---------------------------------------------------
% References: Arturo Estrella (1998) 'A new measure of fit
% for equations with dichotmous dependent variable', JBES,
% Vol. 16, #2, April, 1998.

% written by:
% James P. LeSage, Dept of Economics
% Texas State University-San Marcos
% 601 University Drive
% San Marcos, TX 78666
%jlesage@spatial-econometrics.com
if (nargin < 2); error('Wrong # of arguments to probit'); end;
if (nargin > 4); error('Wrong # of arguments to probit'); end;

% check for all 1's or all 0's
tmp = find(y ==1);
chk = length(tmp); 
[nobs junk] = size(y);
if chk == nobs
   error('probit: y-vector contains all ones');
elseif chk == 0
   error('probit: y-vector contains no ones');
end;

% maximum likelihood probit estimation
result.meth = 'probit';


res = ols(y,x); % use ols values as start
[t k] = size(x);
b = res.beta;

if nargin == 2
tol = 0.000001;
maxit = 100;
elseif nargin ==3
tol = 0.000001;
end;

crit = 1.0;
i = ones(t,1);
tmp1 = zeros(t,k);
tmp2 = zeros(t,k);

iter = 1;

while (iter <= maxit) & (crit > tol)

pdf = norm_pdf(x*b);
cdf = norm_cdf(x*b);

tmp = find(cdf <=0);
[n1 n2] = size(tmp);
if n1 ~= 0
cdf(tmp,1) = 0.00001*ones(length(tmp),1);
end;

tmp = find(cdf >= 1);
[n1 n2] = size(tmp);
if n1 ~= 0
cdf(tmp,1) = 0.99999*ones(length(tmp),1);
end;


% gradient vector for probit (and logit)
term1 = y.*(pdf./cdf);
term2 = (i-y).*(pdf./(i-cdf));

for kk=1:k;
tmp1(:,kk) = term1.*x(:,kk);
tmp2(:,kk) = term2.*x(:,kk);
end;

g = tmp1-tmp2;
gs = (sum(g))';

% compute see page 883 Green, 1997
q = 2*y - i;  
xxb = x*b;
pdf = norm_pdf(q.*xxb);
cdf = norm_cdf(q.*xxb);
lambda = (q.*pdf)./cdf; 
H = zeros(k,k);
for ii=1:t;
xb = x(ii,:)*b;
xp = x(ii,:)';
H = H - lambda(ii,1)*(lambda(ii,1) +xb)*(xp*x(ii,:));
end;

db = -inv(H)*gs;
% stepsize determination
s = 2;
term1 = 0; term2 = 1;
while term2 > term1
 s = s/2;
 term1 = pr_like(b+s*db,y,x);
 term2 = pr_like(b+s*db/2,y,x);
end;

bn = b + s*db;
crit = abs(max(max(db)));
b = bn;
iter = iter + 1;

end; % end of while

if iter >=maxit
fprintf(1,'probit: no convergence in %d iterations \n',iter);
end;

% compute Hessian for inferences
q = 2*y - i;  % see page 883 Green, 1997
xxb = x*b;
pdf = norm_pdf(q.*xxb);
cdf = norm_cdf(q.*xxb);
lambda = (q.*pdf)./cdf; 
H = zeros(k,k);
for i=1:t;
xb = x(i,:)*b;
xp = x(i,:)';
H = H - lambda(i,1)*(lambda(i,1) +xb)*(xp*x(i,:));
end;

% now compute regression results
covb = -inv(H);
stdb = sqrt(diag(covb));
result.tstat = b./stdb;

% fitted probabilities
result.yhat = norm_cdf(x*b);
result.resid = y - result.yhat;

result.sige = (result.resid'*result.resid)/t;

% find ones
tmp = find(y ==1);
P = length(tmp); 
cnt0 = t-P;
cnt1 = P;
P = P/t; % proportion of 1's
like0 = t*(P*log(P) + (1-P)*log(1-P)); % restricted likelihood
like1 = pr_like(b,y,x);              % unrestricted Likelihood

result.r2mf = 1-(abs(like1)/abs(like0)); % McFadden pseudo-R2 


term0 = (2/t)*like0;
term1 = 1/(abs(like1)/abs(like0))^term0;
result.rsqr = 1-term1;  % Estrella R2

result.beta = b;
result.lratio = 2*(like1-like0); % LR-ratio test against intercept model
result.lik = like1; % unrestricted Likelihood
result.nobs = t;    % nobs
result.nvar = k;    % nvars
result.zip = cnt0;  % number of 0's
result.one = cnt1;  % number of 1's
result.iter = iter; % number of iterations
result.convg = crit;% convergence criterion max(max(-inv(H)*g))
result.y = y;       % y data vector

